Anomalies in time series data often produce a high variance of uncertainty prediction that is difficult to predict, thus becoming a challenge for reliable model design [1]}, [2]}. To provide a more reliable forecast during the presence of anomalies, probabilistic forecasting methods are often studied, which can report a level of uncertainty [3]}.
